[Numpy-discussion] Using multiprocessing (shared memory) with numpy array multiplication
Fri Jun 10 08:14:52 CDT 2011
Thanks for getting back to me.
I'm doing element wise multiplication, basically innerProduct =
numpy.sum(array1*array2) where array1 and array2 are, in general,
multidimensional. I need to do many of these operations, and I'd like to
split up the tasks between the different cores. I'm not using numpy.dot, if
I'm not mistaken I don't think that would do what I need.
> Date: Thu, 09 Jun 2011 13:11:40 -0700
> From: Christopher Barker <Chris.Barker@noaa.gov>
> Subject: Re: [Numpy-discussion] Using multiprocessing (shared memory)
> with numpy array multiplication
> To: Discussion of Numerical Python <email@example.com>
> Message-ID: <4DF128FC.firstname.lastname@example.org>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
> Not much time, here, but since you got no replies earlier:
> > > I'm parallelizing some code I've written using the built in
> > multiprocessing
> > > module. In my application, I need to multiply many large arrays
> > together
> is the matrix multiplication, or element-wise? If matrix, then numpy
> should be using LAPACK, which, depending on how its built, could be
> using all your cores already. This is heavily dependent on your your
> numpy (really the LAPACK it uses0 is built.
> > > and
> > > sum the resulting product arrays (inner products).
> are you using numpy.dot() for that? If so, then the above applies to
> that as well.
> I know I could look at your code to answer these questions, but I
> thought this might help.
> Christopher Barker, Ph.D.
> Emergency Response Division
> NOAA/NOS/OR&R (206) 526-6959 voice
> 7600 Sand Point Way NE (206) 526-6329 fax
> Seattle, WA 98115 (206) 526-6317 main reception
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